Federated learning has received a great deal of research attention recently, with privacy
protection becoming a key factor in the development of artificial intelligence. Federated …

The creation and application of data in biomedicine and healthcare often face privacy
constraints, bias, distributional shifts, underrepresentation of certain groups and data …

Generating synthetic data through generative models is gaining interest in the ML
community and beyond, promising a future where datasets can be tailored to individual …

Recent text and image foundation models are incredibly impressive, and these models are
attracting an ever-increasing portion of research resources. In this position piece we aim to …

Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …

The advent of the Internet of Things (IoT) has brought forth an era of unprecedented
connectivity, with an estimated 80 billion smart devices expected to be in operation by the …

Artificial Intelligence Generated Content (AIGC) is one of the latest achievements in AI
development. The content generated by related applications, such as text, images and …

Generating synthetic data through generative models is gaining interest in the ML
community and beyond. In the past, synthetic data was often regarded as a means to private …

Synthetic data promise privacy-preserving data sharing for healthcare research and
development. Compared with other privacy-enhancing approaches—such as federated …

Membership inference (MI) attacks mainly aim to infer whether a data record was used to
train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …